Explaining local policy choices: A Multiple Streams analysis of municipal emergency management
Bibliographic record
Abstract
Abstract: Canadian municipal governments are expected to play a central role in emergency management, which involves developing policies and programs to cope with emergencies and their impacts. But although all communities face potential emergencies, the quality of municipal emergency planning varies considerably from one community to another. This suggests that some municipal decision‐makers have recognized emergencies as a problem and have prioritized this issue relative to others competing for attention and resources. This article examines policy‐making in municipal emergency management through the lens of the Multiple Streams framework, an analytical model that explains how problems are recognized, how and why they are added to the decision agenda, and how they are matched with policy solutions. Sommaire : Les gouvernements municipaux canadiens sont supposés jouer un rôle essentiel dans la gestion des urgences, ce qui implique l'élaboration de politiques et de programmes pour faire face aux urgences et à leurs répercussions. Mais alors que toutes les collectivités font face à des urgences éventuelles, la qualité de la planification des urgences à l'échelle municipale varie considérablement d'une collectivitéà une autre. Cela laisse entendre que certains décisionnaires municipaux ont reconnu les urgences comme étant un problème et ont accordé la prioritéà cette question par rapport à d'autres rivalisant pour obtenir de l'attention et des ressources. Le présent article examine l'élaboration de politiques dans le domaine de la gestion des urgences dans une municipalité par le biais du cadre Sources multiples, un modèle analytique qui explique comment les problèmes sont reconnus, comment et pourquoi ils sont ajoutés au programme de décisions, et quelles sont les solutions en matière de politiques pour les résoudre.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".